hive-mind-advanced

Orchestrate multi-agent coordination with queen-led hierarchies and consensus voting.

Updated Aug 23, 2026

Search Tags:#AI agents #multi-agent #consensus #collective intelligence #swarm #coordination

Ready to run

npx skills add https://github.com/chrislemke/stoffy --skill hive-mind-advanced-chrislemke

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: hive-mind-advanced
Source: https://github.com/chrislemke/stoffy/tree/main/.claude/skills/hive-mind-advanced
Command: npx skills add https://github.com/chrislemke/stoffy --skill hive-mind-advanced-chrislemke

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) and assets (resource) components.

What problem does it solve?

This Skill provides a sophisticated system for coordinating multiple AI agents in complex tasks, enabling collective intelligence and efficient problem-solving.

Core Features & Use Cases

Queen-Led Architecture: Orchestrate agents with strategic, tactical, or adaptive queen coordinators.
Specialized Workers: Utilize diverse agent roles like researchers, coders, and testers.
Collective Memory: Maintain a shared, persistent knowledge base for all agents.
Consensus Mechanisms: Ensure robust decision-making with majority, weighted, or Byzantine fault-tolerant voting.
Use Case: Coordinate a team of AI agents to build a complex software architecture, with a strategic queen defining the plan, coder agents implementing features, and a collective memory storing design patterns and past decisions.

Quick Start

Spawn a new hive mind swarm to build a microservices architecture.

Frequently Asked Questions about hive-mind-advanced

High-intent search queries and answers about installing and using this skill.

FAQPage Schema

What is multi-agent coordination for complex project execution?▼

Multi-agent coordination uses a queen-led hierarchical architecture with specialized workers to execute complex software projects. Strategic, tactical, or adaptive queens define plans while worker agents implement features, enabling distributed collaboration.

How do I coordinate AI agents to build a software architecture?▼

You coordinate AI agents by spawning a hive mind swarm with a strategic queen to define the plan and specialized workers to implement features. The system manages collective memory to store design patterns and past decisions during software architecture creation.

How does collective memory work in a multi-agent system?▼

Collective memory maintains a shared, persistent knowledge base for all agents in the multi-agent system. It enables shared knowledge persistence and retrieval, allowing specialized workers to access design patterns and past decisions during project execution.

What consensus mechanisms are available for AI swarm decision-making?▼

AI swarm decision-making supports majority, weighted, and Byzantine fault-tolerant voting mechanisms. These consensus mechanisms ensure robust distributed decision-making across specialized worker agents within the queen-led hierarchical architecture.

Can I use specialized AI agent roles like researchers and coders for task execution?▼

Yes, the system utilizes diverse specialized agent roles including researchers, coders, and testers for task execution. These specialized workers operate under a queen-led architecture to facilitate complex project execution through distributed collaboration.

What are the limitations of queen-led hierarchical AI agent coordination?▼

Queen-led hierarchical AI agent coordination relies on a central queen coordinator, meaning system performance is bounded by the queen's strategic, tactical, or adaptive coordination capabilities. Complex project execution depends heavily on effective consensus mechanisms and collective memory retrieval.